Abstract
In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.
Highlights
Continued urbanization leads to a deterioration of the urban thermal environment and serial ecological consequences
To obtain accurate information on the radiation received by the sensors, the digital number (DN) values of Landsat-8 data were converted to emissivity by radiometric calibration
It can be seen from the figure that the Land Surface Temperature (LST) in the northwest regions are higher than other areas, and the comparison between these regions and the land cover map reveals that the regions are densely built-up areas in the urban center, mainly artificial surfaces, with a high urbanization level; human activities and natural factors caused the changes in some feature types
Summary
Continued urbanization leads to a deterioration of the urban thermal environment and serial ecological consequences. LST is one of the most important parameters for characterizing land surface energy balance and water cycle processes at both regional and global scales, which is essential in the hydrological cycle and climate prediction It is widely used in various fields, including natural disaster monitoring and prevention, heat island effect assessment, crop yield estimation, vegetation monitoring, climate changes, etc. Many satellites carry thermal infrared sensors, such as MODIS/Terra and Aqua, advanced very high-resolution radiometer (AVHRR)/NOAA, and Landsat-8/TIRS [13]. Multi-Channel Algorithm, and Hyperspectral Algorithm [15,16,17,18,19,20] These LST retrieval algorithms require several corresponding parameters, including the land surface emissivity (LSE), effective mean atmospheric temperature, and atmospheric transmittance. In order to provide a valuable reference for the selection of LST retrieval algorithms for different periods in Shanghai, this study analyzed and compared the accuracy of retrieval results of the RTE, MWA, SWA, and SCA, testing the suitability of these algorithms for LST retrieval by using the Landsat-8 remote sensing data in winter and summer
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